(test_x, test_y) = (np.load(".\\npy\\NRR_x_9339in2334.npy"),
np.load(".\\npy\\NRR_y_9339in2334.npy"))
train_x = np.concatenate([train_x_90, train_x_180], axis=0)
train_y = np.concatenate([train_y_90, train_y_180], axis=0)
#print("test x shape {}".format(test_x.shape))
test_data_start = training_num + 1
input_size = (256, 256, 4)
for i in range(len(name)):
# print("Train data shape {}\n{}".format(train_x.shape, train_y.shape))
print("Building model.")
input_shape = (256, 256, 3)
model_select = model.UNet_DtoU5(block=model.RDBlocks,
name="unet_2RD-5",
input_size=input_shape,
block_num=2)
print("Loading data.")
if i == 0:
print("EX high data")
excel_file = ".\\result\\data\\20201118_256_51984_UNet(2RDB8-DtoU-5)_CETrainData_iou.xlsx"
total_num = 51984
(train_x, train_y) = data.extract_high_result(
np.load(".\\npy\\V1_start1_total51984_size256_x.npy"),
np.load(".\\npy\\V1_start1_total51984_size256_y.npy"),
excel_file,
total_num,
threshold=0.8)
excel_file = ".\\result\\data\\20201118_256_51984_UNet(2RDB8-DtoU-5)_CE_iou.xlsx"
total_num = 12996
(test_x, test_y) = data.extract_high_result(
cv2.drawContours(result[index], contours, contour, 1, -1)
result[index] = np.expand_dims(cv2.dilate(result[index],
kernel,
iterations=1),
axis=-1)
return result
if __name__ == "__main__":
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
(train_x, train_y) = (np.load(".\\npy\\V3.2_x_1in7131.npy"),
np.load(".\\npy\\V3.2_y_1in7131.npy"))
model_select = model.UNet_DtoU5(block=model.RDBlocks,
input_size=(256, 256, 4),
n_layers_per_block=8,
block_num=2)
model_select.load_weights(
".\\result\model_record\V3.2_test\\20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE\\20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE.h5"
)
test_flag = 1
batch = 3
epoch = 50
print("model building.")
# model_build = model.model(model=model_select, name="20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE", size=(256, 256, 4))
print("model building.")
model_build = model.model(
model=model_select,
name="20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE",
size=(256, 256, 4))